Johnston Sean B, Raines Ronald T
1 Department of Biochemistry, University of Wisconsin-Madison , Madison, Wisconsin.
2 Department of Chemistry, University of Wisconsin-Madison , Madison, Wisconsin.
J Comput Biol. 2016 Dec;23(12):969-975. doi: 10.1089/cmb.2016.0058. Epub 2016 Jun 16.
Connecting a genotype with a phenotype can provide immediate advantages in the context of modern medicine. Especially useful would be an algorithm for predicting the impact of nonsynonymous single-nucleotide polymorphisms in the gene for PTEN, a protein that is implicated in most human cancers and connected to germline disorders that include autism. We have developed a protein impact predictor, PTENpred, that integrates data from multiple analyses using a support vector machine algorithm. PTENpred can predict phenotypes related to a human PTEN mutation with high accuracy. The output of PTENpred is designed for use by biologists, clinicians, and laymen, and features an interactive display of the three-dimensional structure of PTEN. Using knowledge about the structure of proteins, in general, and the PTEN protein, in particular, enables the prediction of consequences from damage to the human PTEN gene. This algorithm, which can be accessed online, could facilitate the implementation of effective therapeutic regimens for cancer and other diseases.
将基因型与表型联系起来在现代医学背景下能带来直接优势。对于预测PTEN基因中非同义单核苷酸多态性的影响而言，一种算法会特别有用，PTEN是一种与大多数人类癌症相关且与包括自闭症在内的种系疾病有关的蛋白质。我们开发了一种蛋白质影响预测工具PTENpred，它使用支持向量机算法整合来自多种分析的数据。PTENpred能够高精度地预测与人类PTEN突变相关的表型。PTENpred的输出旨在供生物学家、临床医生和外行使用，其特点是交互式展示PTEN的三维结构。一般来说，利用关于蛋白质结构的知识，特别是关于PTEN蛋白的知识，能够预测人类PTEN基因受损的后果。这种可在线获取的算法有助于实施针对癌症和其他疾病的有效治疗方案。
